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With the rapid development in the field of embodied intelligence, robots require precise motion control and a comprehensive understanding of visual and semantic cues in dynamic environments. Before the advent of Vision-Language-Action (VLA) models, the systems for vision, language, and action in artificial intelligence were developed in silos, creating a significant gap between perception, cognition, and physical execution. VLA models address this by constructing an end-to-end unified computational framework that integrates visual perception, language understanding, and robot action generation, thereby significantly enhancing a robot’s ability to adapt and perform in open, dynamic worlds. This paper explores the latest advancements in Vision-Language-Action (VLA) models within the robotics domain. To address the slow inference speeds that are a common issue in current mainstream VLA models, we propose an innovative solution based on flow matching and hierarchical inference. During inference, this model employs a two-tiered, “Chain-of-Thought”-style reasoning mechanism. Upon receiving a high-level instruction from the user, the model first utilizes a high-level cognitive module to decompose it into a clear, textual sub-task. Subsequently, this generated sub-task serves as a direct input for the low-level execution module, which then generates the specific sequence of physical actions required to complete the step. This seamless transition from abstract planning to concrete execution is accomplished entirely within a single model, markedly improving both inference speed and execution efficiency. To ensure the model’s generalization capabilities, the pre-training phase incorporates a fusion of robot operational data and large-scale multimodal web data, which strengthens its logical planning abilities. This model architecture effectively enhances the reasoning and generalization performance of VLA models, demonstrating the immense potential of end-to-end learning systems in solving real-world, complex, long-horizon tasks.

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Research on Hierarchical Reasoning Method Based on Flow Matching

  • 会议论文

  • 发表日期：

    2026年

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  • Yongkang Li,
  • Chuanlei Zhang,
  • Yuliang Zhang,
  • Qunwei Song,
  • Jianrong Li,
  • JunZhang,
  • Ruihan Xu

摘要

With the rapid development in the field of embodied intelligence, robots require precise motion control and a comprehensive understanding of visual and semantic cues in dynamic environments. Before the advent of Vision-Language-Action (VLA) models, the systems for vision, language, and action in artificial intelligence were developed in silos, creating a significant gap between perception, cognition, and physical execution. VLA models address this by constructing an end-to-end unified computational framework that integrates visual perception, language understanding, and robot action generation, thereby significantly enhancing a robot’s ability to adapt and perform in open, dynamic worlds. This paper explores the latest advancements in Vision-Language-Action (VLA) models within the robotics domain. To address the slow inference speeds that are a common issue in current mainstream VLA models, we propose an innovative solution based on flow matching and hierarchical inference. During inference, this model employs a two-tiered, “Chain-of-Thought”-style reasoning mechanism. Upon receiving a high-level instruction from the user, the model first utilizes a high-level cognitive module to decompose it into a clear, textual sub-task. Subsequently, this generated sub-task serves as a direct input for the low-level execution module, which then generates the specific sequence of physical actions required to complete the step. This seamless transition from abstract planning to concrete execution is accomplished entirely within a single model, markedly improving both inference speed and execution efficiency. To ensure the model’s generalization capabilities, the pre-training phase incorporates a fusion of robot operational data and large-scale multimodal web data, which strengthens its logical planning abilities. This model architecture effectively enhances the reasoning and generalization performance of VLA models, demonstrating the immense potential of end-to-end learning systems in solving real-world, complex, long-horizon tasks.